Evaluating the prevalence of spurious correlations in pulsar timing array data sets

Abstract

Pulsar timing array collaborations have recently reported evidence for a noise process with a common spectrum among the millisecond pulsars in the arrays. The spectral properties of this common-noise process are consistent with expectations for an isotropic gravitational-wave background (GWB) from inspiralling supermassive black hole binaries. However, recent simulation analyses based on Parkes Pulsar Timing Array data indicate that such a detection may arise spuriously. In this paper, we use simulated pulsar timing array data sets to further test the robustness of the inference methods for spectral and spatial correlations from a GWB. Expanding on our previous results, we find strong support (Bayes factors exceeding 105) for the presence of a common-spectrum noise process in data sets where no common process is present, under a wide range of timing noise prescriptions per pulsar. We show that these results are highly sensitive to the choice of Bayesian priors on timing noise parameters, with priors that more closely match the injected distributions of timing noise parameters resulting in diminished support for a common-spectrum noise process. These results emphasize shortcomings in current methods for inferring the presence of a common-spectrum process, and imply that the detection of a common process is not a reliable precursor to detection of the GWB. Future searches for the nanohertz GWB should remain focused on detecting spatial correlations, and make use of more tailored specifications for a common-spectrum noise process.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 03, 2022
Source ID
10.1093/mnras/stac2100

Entities

People

  • A. Cameron
  • Andrew Zic
  • Boris Goncharov
  • Christopher J. Russell
  • Daniel Reardon
  • George Hobbs
  • Joanne Dawson
  • Matthew Kerr
  • N D Ramesh Bhat
  • Nithyanandan Thyagarajan
  • R. N. Manchester
  • Rami Mandow
  • Ryan M. Shannon
  • Shi Dai
  • Tommy Marshman
  • Xing-Jiang Zhu

Organizations

  • Australian Research Council
  • Australian Technology Park
  • Beijing Normal University
  • Commonwealth Scientific and Industrial Research Organisation
  • Curtin University
  • Gran Sasso Science Institute
  • Istituto Nazionale di Fisica Nucleare
  • Macquarie University
  • Ministry of Education, Universities and Research
  • National Aeronautics and Space Administration
  • National Astronomical Observatory of China
  • Swinburne University of Technology
  • United States Naval Research Laboratory
  • Western Sydney University

Tags

Fields of Study

  • Physics

Readers

  • Astronomy/Astrophysics
  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference